Internet of Things devices introduce significant security challenges caused by their heterogeneous and resource-constrained nature in many sectors, such as healthcare, industry, education, and agriculture. Intrusion Detection Systems (IDS)s serve a key role in identifying malicious activities in such environments; traditional approaches cannot often capture dynamic interactions and temporal correlations in network traffic. In this research, we propose a novel hybrid IDS technique utilizing Long Short-Term Memory (LSTM) networks in conjunction with Lotka-Volterra (LV) dynamic modeling. The LSTM component is employed to learn temporal patterns and estimate system states from IoT traffic, while the LV model captures the dynamic interaction between normal and malicious behavior. We introduce a mathematically based decision mechanism on an anomaly score for effective classification. The model’s results on the CICIIoT 2025 dataset achieve an accuracy of 99.85, outperforming other models, and have a detection time of 47 ms and a reduced-complexity algorithm. These results highlight the effectiveness of combining artificial intelligence with dynamic system modeling for intrusion detection in IoT environments, providing a promising direction for future research in intelligent cybersecurity systems.